Calculation of weight of partitions in pooled solution parameters for intermediate solution

wop_inter calculates the weight of partitions in the pooled solution parameters (consistency, coverage) for the intermediate solution.

wop_inter(
  dataset,
  units,
  time,
  cond,
  out,
  n_cut,
  incl_cut,
  intermediate,
  amb_selector
)

Arguments

dataset	Calibrated pooled dataset for partitioning and minimization
units	Units defining the within-dimension of data (time series)
time	Periods defining the between-dimension of data (cross sections)
cond	Conditions used for the pooled analysis
out	Outcome used for the pooled analysis
n_cut	Frequency cut-off for designating truth table rows as observed
incl_cut	Inclusion cut-off for designating truth table rows as consistent
intermediate	A vector of directional expectations to derive the intermediate solutions
amb_selector	Numerical value for selecting a single model in the presence of model ambiguity. Models are numbered according to their order produced by `minimize` by the `QCA` package.

A dataframe with information about the weight of the partitions for pooled consistency and coverage scores and the following columns:

type: The type of the partition. between stands for cross-sections; within stands for time series. pooled stands information about the pooled data.
partition: Type of partition. For between-dimension, the unit identifiers are listed (argument units). For the within-dimension, the time identifiers are listed (argument time). The entry is - for the pooled data.
denom_cons: Denominator of the consistency formula. It is the sum over the cases' membership in the solution.
num_cons: Numerator of the consistency formula. It is the sum over the minimum of the cases' membership in the solution and the outcome.
denom_cov: Denominator of the coverage formula. It is the sum over the cases' membership in the outcome.
num_cov: Numerator of the coverage formula. It is the sum over the minimum of the cases' membership in the solution and the outcome. (identical to num_cons)

data(Schwarz2016)